BoostFGL: Boosting Fairness in Federated Graph Learning

• URL: http://arxiv.org/abs/2601.16496v1
• Date: Fri, 23 Jan 2026 06:54:48 GMT
• Title: BoostFGL: Boosting Fairness in Federated Graph Learning
• Authors: Zekai Chen, Kairui Yang, Xunkai Li, Henan Sun, Zhihan Zhang, Jia Li, Qiangqiang Dai, Rong-Hua Li, Guoren Wang,
• Abstract summary: Federated graph learning (FGL) enables collaborative training of graph neural networks (GNNs) across decentralized subgraphs without exposing raw data.<n>We show that existing FGL methods often achieve high overall accuracy, but can conceal severe degradation on disadvantaged node groups.<n>We propose BoostFGL, a boosting-style framework for fairness-aware FGL.
• Score: 41.975669720893436
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Federated graph learning (FGL) enables collaborative training of graph neural networks (GNNs) across decentralized subgraphs without exposing raw data. While existing FGL methods often achieve high overall accuracy, we show that this average performance can conceal severe degradation on disadvantaged node groups. From a fairness perspective, these disparities arise systematically from three coupled sources: label skew toward majority patterns, topology confounding in message propagation, and aggregation dilution of updates from hard clients. To address this, we propose \textbf{BoostFGL}, a boosting-style framework for fairness-aware FGL. BoostFGL introduces three coordinated mechanisms: \ding{182} \emph{Client-side node boosting}, which reshapes local training signals to emphasize systematically under-served nodes; \ding{183} \emph{Client-side topology boosting}, which reallocates propagation emphasis toward reliable yet underused structures and attenuates misleading neighborhoods; and \ding{184} \emph{Server-side model boosting}, which performs difficulty- and reliability-aware aggregation to preserve informative updates from hard clients while stabilizing the global model. Extensive experiments on 9 datasets show that BoostFGL delivers substantial fairness gains, improving Overall-F1 by 8.43\%, while preserving competitive overall performance against strong FGL baselines.

Related papers

• You Can Trust Your Clustering Model: A Parameter-free Self-Boosting Plug-in for Deep Clustering [73.48306836608124]
  DCBoost is a parameter-free plug-in designed to enhance the global feature structures of current deep clustering models.<n>By harnessing reliable local structural cues, our method aims to elevate clustering performance effectively.
  arXiv  Detail & Related papers  (2025-11-26T09:16:36Z)
• FedHERO: A Federated Learning Approach for Node Classification Task on Heterophilic Graphs [55.51300642911766]
  Federated Graph Learning (FGL) empowers clients to collaboratively train Graph neural networks (GNNs) in a distributed manner.<n>FGL methods usually require that the graph data owned by all clients is homophilic to ensure similar neighbor distribution patterns of nodes.<n>We propose FedHERO, an FGL framework designed to harness and share insights from heterophilic graphs effectively.
  arXiv  Detail & Related papers  (2025-04-29T22:23:35Z)
• Towards Unbiased Federated Graph Learning: Label and Topology Perspectives [30.103014192157115]
  Federated Graph Learning (FGL) enables privacy-preserving, distributed training of graph neural networks without sharing raw data.<n>We propose FairFGL, a novel framework that enhances fairness through fine-grained graph mining and collaborative learning.
  arXiv  Detail & Related papers  (2025-04-14T08:00:20Z)
• SpreadFGL: Edge-Client Collaborative Federated Graph Learning with Adaptive Neighbor Generation [16.599474223790843]
  Federated Graph Learning (FGL) has garnered widespread attention by enabling collaborative training on multiple clients for classification tasks. We propose a novel FGL framework, named SpreadFGL, to promote the information flow in edge-client collaboration. We show that SpreadFGL achieves higher accuracy and faster convergence against state-of-the-art algorithms.
  arXiv  Detail & Related papers  (2024-07-14T09:34:19Z)
• An Aggregation-Free Federated Learning for Tackling Data Heterogeneity [50.44021981013037]
  Federated Learning (FL) relies on the effectiveness of utilizing knowledge from distributed datasets. Traditional FL methods adopt an aggregate-then-adapt framework, where clients update local models based on a global model aggregated by the server from the previous training round. We introduce FedAF, a novel aggregation-free FL algorithm.
  arXiv  Detail & Related papers  (2024-04-29T05:55:23Z)
• AdaFGL: A New Paradigm for Federated Node Classification with Topology Heterogeneity [44.11777886421429]
  Federated Graph Learning (FGL) has attracted significant attention as a distributed framework based on graph neural networks. We introduce the concept of structure Non-iid split and then present a new paradigm called underlineAdaptive underlineFederated underlineGraph underlineLearning (AdaFGL) Our proposed AdaFGL outperforms baselines by significant margins of 3.24% and 5.57% on community split and structure Non-iid split, respectively.
  arXiv  Detail & Related papers  (2024-01-22T08:23:31Z)
• Chasing Fairness in Graphs: A GNN Architecture Perspective [73.43111851492593]
  We propose textsfFair textsfMessage textsfPassing (FMP) designed within a unified optimization framework for graph neural networks (GNNs) In FMP, the aggregation is first adopted to utilize neighbors' information and then the bias mitigation step explicitly pushes demographic group node presentation centers together. Experiments on node classification tasks demonstrate that the proposed FMP outperforms several baselines in terms of fairness and accuracy on three real-world datasets.
  arXiv  Detail & Related papers  (2023-12-19T18:00:15Z)
• Tackling the Local Bias in Federated Graph Learning [48.887310972708036]
  In Federated graph learning (FGL), a global graph is distributed across different clients, where each client holds a subgraph. Existing FGL methods fail to effectively utilize cross-client edges, losing structural information during the training. We propose a novel FGL framework to make the local models similar to the model trained in a centralized setting.
  arXiv  Detail & Related papers  (2021-10-22T08:22:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.